Feature Articles

Metabolomics Research Picks Up Speed

Field Advances in Quest to Improve Disease Diagnosis and Predict Drug Response

Interactive Metabolomics

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Researchers at the University of Nottingham use diffusion-edited nuclear magnetic resonance spectroscopy to assess the effects of a biological matrix on metabolites. Diffusion-edited NMR experiments provide a way to separate the different compounds in a mixture based on the differing translational diffusion coefficients (which reflect the size and shape of the molecule). The measurements are carried out by observing the attenuation of the NMR signals during a pulsed field gradient experiment.

Clare Daykin, Ph.D., is a lecturer at the University of Nottingham, U.K. Her field of investigation encompasses “interactive metabolomics,” a tool she has developed through research funded by the Engineering and Physical Sciences Research Council. She defines the approach as “the study of the interactions between low molecular weight biochemicals and macromolecules in biological samples such as blood plasma, without preselection of the components of interest.

“Blood plasma is a heterogeneous mixture of molecules that undergo a variety of interactions including metal complexation, chemical exchange processes, micellar compartmentation, enzyme-mediated biotransformations, and small molecule–macromolecular binding.”

Many low molecular weight compounds can exist freely in solution, bound to proteins, or within organized aggregates such as lipoprotein complexes. Therefore, quantitative comparison of plasma composition from diseased individuals compared to matched controls provides an incomplete insight to plasma metabolism.

“It is not simply the concentrations of metabolites that must be investigated, but their interactions with the proteins and lipoproteins within this complex web. You have to look at the other components and how they influence one another,” Dr. Daykin explains.

Rather than targeting specific metabolites of interest, Dr. Daykin’s metabolite–protein binding studies aim to study the interactions of all detectable metabolites within the macromolecular sample. Such activities can be studied through the use of diffusion-edited nuclear magnetic resonance (NMR) spectroscopy, in which one can assess the effects of the biological matrix on the metabolites. “This can lead to a more relevant and exact interpretation for systems where metabolite–macromolecule interactions occur.”

Diffusion-edited NMR experiments provide a way to separate the different compounds in a mixture based on the differing translational diffusion coefficients (which reflect the size and shape of the molecule). The measurements are carried out by observing the attenuation of the NMR signals during a pulsed field gradient experiment.

Pushing the Limits

It is widely recognized that many drug candidates fail during development due to ancillary toxicity. Uwe Sauer, Ph.D., professor, and Nicola Zamboni, Ph.D., researcher, both at the Eidgenössische Technische Hochschule, Zürich (ETH Zürich), are applying high-throughput intracellular metabolomics to understand the basis of these unfortunate events and head them off early in the course of drug discovery.

“Since metabolism is at the core of drug toxicity, we developed a platform for measurement of 50–100 targeted metabolites by a high-throughput system consisting of flow injection coupled to tandem mass spectrometry.”

Using this approach, Dr. Sauer’s team focused on the central metabolism of the yeast Saccharomyces cerevisiae, reasoning that this core network would be most susceptible to potential drug toxicity. Screening approximately 41 drugs that were administered at seven concentrations over three orders of magnitude, they observed changes in metabolome patterns at much lower drug concentrations without attendant physiological toxicity.

The group carried out statistical modeling of about 60 metabolite profiles for each drug they evaluated. This data allowed the construction of a “profile effect map” in which the influence of each drug on metabolite levels can be followed, including off-target effects, which provide an indirect measure of the possible side effects of the various drugs.

“We have found that this approach is at least 100 times as fast as other omics screening platforms,” Dr. Sauer says. “Some drugs, including many anticancer agents, disrupt metabolism long before affecting growth.”

Furthermore, they used the principle of 13C-based flux analysis, in which metabolites labeled with 13C are used to follow the utilization of metabolic pathways in the cell. These 13C-determined intracellular responses of metabolic fluxes to drug treatment demonstrate the functional performance of the network to be rather robust, leading Dr. Sauer to the conclusion that the phenotypic vigor he observes to drug challenges is achieved by a flexible make up of the metabolome.

Dr. Sauer is confident that it will be possible to expand the scope of these investigations to hundreds of thousands of samples per study. This will allow answers to the questions of how cells establish a stable functioning network in the face of inevitable concentration fluctuations.

Is Now the Hour?

There is great enthusiasm and agitation within the biotech community for metabolomics approaches as a means of reversing the dismal record of drug discovery that has accumulated in the last decade. While the concept clearly makes sense and is being widely applied today, there are many reasons why drugs fail in development, and metabolomics will not be a panacea for resolving all of these questions. It is too early at this point to recognize a trend or a track record, and it will take some time to see how this approach can aid in drug discovery and shorten the timeline for the introduction of new pharmaceutical agents.

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